{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.3 Matplotlib数据可视化基础"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.3.1 基本图形绘制**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1) 折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = [1, 2, 3]\n",
    "y = [2, 4, 6]\n",
    "plt.plot(x, y)  # 绘制折线图\n",
    "import pylab as pl\n",
    "pl.xticks(rotation=45)\n",
    "plt.show()  # 展示图形"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PyLab 是一个面向 Matplotlib 的绘图库接口，其语法和 MATLAB 十分相近。PyLab 是一个单独的模块，随 Matplotlib 软件包一起安装"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果想让x和y之间有些数学关系，列表是不太容易进行数学运算的，这时候就可以通过2.1.2小节所讲的Numpy库引入一维数组进行数学运算，代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x1 = np.array([1, 2, 3])\n",
    "\n",
    "# 第一条线：y = x + 1\n",
    "y1 = x1 + 1\n",
    "plt.plot(x1, y1) # 使用默认参数画图\n",
    "\n",
    "# 第二条线：y = x*2\n",
    "y2 = x1*2\n",
    "# color设置颜色，linewidth设置线宽，单位像素，linestyle默认为实线，“--”表示虚线\n",
    "plt.plot(x1, y2, color='red', linewidth=3, linestyle='--')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(2) 柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = [1, 2, 3, 4, 5]\n",
    "y = [5, 4, 3, 2, 1]\n",
    "plt.bar(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(3) 散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.random.rand(10)\n",
    "y = np.random.rand(10)\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(4) 直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gSO3gyF2SWshwl6QWMtwlqYUMd0lqoUEekP3pJBeSvNDRdnuSw0leara3dby2N8nJJCeS3L9ShUuSFjfIyP33gG0L2vYAR6pqE3CkOSbJZmAHcHdzzmNJ1gytWknjr8d67671fv0M8oDsryTZsKB5O/CeZv8A8DTwSNP+RFVdBF5OchLYAnx1SPVKGnc91nv3Utvr51rn3O+qqnMAzfbOpn0tcLqj32zTdpUku5LMJJmZm5u7xjLUTa+blLxRSboxDPsmpm7JUd06VtV+YD/A9PR01z66Nr1uUgJHT9KN4FpH7q8kmQRothea9llgXUe/KeDstZcnSboW1xruh4Cdzf5O4KmO9h1JbkmyEdgEHF1eiZKkpeo7LZPkceY/PL0jySzw68CjwMEkDwOngIcAqupYkoPAi8AlYHdVXV6h2iVJixjkapkPLfLS1kX67wP2LacoSS3VXCa5mLeuXce52VPXsaD2clVISddPj8skwQ/7h8nlBySphQx3SWohw13S+OixdIHLFyyNc+6r0OTUes6fOd2/o7TaOCc/NIb7KuQdqJL6cVpGklrIcJekFjLcJamFDHdJaiHDXZJayHCXpBYy3Eek39OSbrrl9T5JSdI18zr3ERnkWnWfQykt0GNVSVeUvJLhLmn18OHbA3NaRlI7uC7NFRy5S2oH16W5giP3FdTrQ1NJ11mPkX0bR/UrNnJPsg34XWAN8MmqenSlvteoDLI6o/OD0pi4webrVyTck6wB/iPwfmAW+FqSQ1X14kp8v1FxdUapJfo823XN627l8o/+36Kvj+OVOis1ct8CnKyq7wAkeQLYDlz3cO83uu71h9bvD1RSSwwwX9/z9d/65UV/OYzqF0OqavhvmvxjYFtV/fPm+CPA36+qj3b02QXsag7fDpwY4K3vAP58yOUOk/Utj/Utj/Utz2qs76eqaqJb55UauXf7FXbFb5Gq2g/sX9KbJjNVNb2cwlaS9S2P9S2P9S1P2+pbqatlZoF1HcdTwNkV+l6SpAVWKty/BmxKsjHJ64AdwKEV+l6SpAVWZFqmqi4l+SjwJ8xfCvnpqjo2hLde0jTOCFjf8ljf8ljf8rSqvhX5QFWSNFreoSpJLWS4S1ILrdpwT/Kvk1SSO0ZdS6ck/zbJN5M8n+SLSd426po6JfnNJN9uanwyyVtGXVOnJA8lOZbkx0nG4rK0JNuSnEhyMsmeUdezUJJPJ7mQ5IVR17JQknVJvpzkePPn+rFR19Qpya1Jjib5RlPfb4y6pm6SrEny9SSL30m1wKoM9yTrmF/aYLzu9533m1X1s1V1D/B54N+MuJ6FDgPvqKqfBf4HsHfE9Sz0AvCPgK+MuhC4YimNDwCbgQ8l2Tzaqq7ye8C2URexiEvAr1TVzwD3ArvH7L/fReC9VfVO4B5gW5J7R1tSVx8Dji/lhFUZ7sC/B36VBTdGjYOq+kHH4RsZsxqr6otVdak5/O/M34MwNqrqeFUNcrfy9fLXS2lU1Y+A15bSGBtV9RXgL0ZdRzdVda6qnmv2/5L5gFo72qr+Rs37q+bw5uZrrP7OJpkCfgn45FLOW3XhnuQB4ExVfWPUtSwmyb4kp4F/wviN3Dv9M+C/jbqIMbcW6FycaJYxCqfVJMkG4F3AMyMu5QrNlMfzwAXgcFWNVX3A7zA/mP3xUk4ay4d1JPkS8NYuL30C+DXgF65vRVfqVV9VPVVVnwA+kWQv8FHg18epvqbPJ5j/J/NnrmdtzffuW98Y6buUhvpL8ibgj4CPL/jX7chV1WXgnubzpyeTvKOqxuLziyQfBC5U1bNJ3rOUc8cy3Kvqfd3ak/xdYCPwjWYFtinguSRbqur8qOvr4g+AP+Y6h3u/+pLsBD4IbK0R3OiwhP9+48ClNJYpyc3MB/tnqupzo65nMVX1/SRPM//5xViEO3Af8ECSXwRuBd6c5Per6sP9TlxV0zJV9a2qurOqNlTVBub/4r37egZ7P0k2dRw+AHx7VLV00zxE5RHggar64ajrWQVcSmMZMj8K+xRwvKp+e9T1LJRk4rUrxpK8HngfY/R3tqr2VtVUk3c7gD8dJNhhlYX7KvFokheSfJP56aOxuvQL+A/ATwKHm8s1//OoC+qU5JeTzAI/B/xxkj8ZZT3Nh8+vLaVxHDg4pKU0hibJ48BXgbcnmU3y8Khr6nAf8BHgvc3/b883o9BxMQl8ufn7+jXm59wHvtxwnLn8gCS1kCN3SWohw12SWshwl6QWMtwlqYUMd0lqIcNdklrIcJekFvr/qWw2rYibhkUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np  \n",
    "\n",
    "# 随机生成10000个服从正态分布的数据\n",
    "data = np.random.randn(10000)\n",
    "\n",
    "# 绘制直方图，bins为颗粒度，即直方图的长条形数目，edgecolor为长条形边框颜色\n",
    "plt.hist(data, bins=40, edgecolor='black')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**补充知识点：在pandas库中的快捷绘图技巧**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'0'}>]], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 这种写法只适合pandas中的DataFrame，不能直接用于Numpy的数组\n",
    "import pandas as pd\n",
    "df = pd.DataFrame(data)  # 将绘制直方图中的data数组转换成DataFrame()格式\n",
    "df.hist(bins=40, edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 此外，除了写df.hist()外，还可以通过下面这种pandas库里的通用绘图代码绘图：\n",
    "df.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里是通过设置kind参数为hist来绘制直方图，通过这种通用绘图代码，pandas库除了可以便捷的绘制直方图外，它还可以通过设置kind参数快捷地绘制其他图形，演示代码如下，首先通过2.2.1节的知识点创建一个二维DataFrame表格df。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>人均收入</th>\n",
       "      <th>人均支出</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>8000</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>7000</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广州</th>\n",
       "      <td>6500</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    人均收入  人均支出\n",
       "北京  8000  6000\n",
       "上海  7000  5000\n",
       "广州  6500  4000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame([[8000, 6000], [7000, 5000], [6500, 4000]], columns=['人均收入', '人均支出'], index=['北京', '上海', '广州'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 此时可以通过pandas同时绘制折线图或者柱状图，代码如下：\n",
    "#plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "#plt.rcParams['axes.unicode_minus'] = False  # 解决负号'-'显示为方块的问题\n",
    "\n",
    "df['人均收入'].plot(kind='line')  # kind=line绘制折线图，不设置则默认折线图\n",
    "df['人均收入'].plot(kind='bar')  # kind=bar绘制柱状图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.3.2 数据可视化常见小技巧**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1) 添加文字说明"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 通过plt.title(name)给图画添加标题；通过plt.xlable()，plt.ylable()用于添加x轴和y轴标签。\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [1, 2, 3]\n",
    "y = [2, 4, 6]\n",
    "plt.plot(x, y)\n",
    "plt.title('TITLE')  # 添加标题\n",
    "plt.xlabel('X')  # 添加X轴标签\n",
    "plt.ylabel('Y')  # 添加Y轴标签\n",
    "plt.show()  # 显示图片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(2) 添加图例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 通过plt.legend()来添加图例，添加前需要设置好lable（标签）参数，代码如下：\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 第一条线, 设定标签lable为y = x + 1\n",
    "x1 = np.array([1, 2, 3])\n",
    "y1 = x1 + 1\n",
    "plt.plot(x1, y1, label='y = x + 1') \n",
    "\n",
    "# 第二条线, 设定标签lable为y = x*2\n",
    "y2 = x1*2\n",
    "plt.plot(x1, y2, color='red', linestyle='--', label='y = x*2')\n",
    "\n",
    "plt.legend(loc='upper left') # 图例位置设置为左上角\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(3) 设置双坐标轴"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的例子可以在一张图里画出两条线，但如果两条线的取值范围相差比较大，那么画出来的图效果便不太好，那么此时如何来画出两条y坐标轴呢？可以在画完第一个图之后，写如下一行代码即可设置双坐标轴。\n",
    "\n",
    "plt.twinx()\n",
    "\n",
    "需要注意的是如果设置了双坐标轴，那么添加图例的时候，每画一次图就得添加一次，而不能在最后统一添加。这里以y = x和y = x^2为例，演示下如何设置双坐标轴，代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 第一条线, 设定标签lable为y = x\n",
    "x1 = np.array([10, 20, 30])\n",
    "y1 = x1\n",
    "plt.plot(x1, y1, color='red', linestyle='--', label='y = x')\n",
    "plt.legend(loc='upper left')  # 该图图例设置在左上角\n",
    "\n",
    "plt.twinx()  # 设置双坐标轴\n",
    "\n",
    "# 第二条线, 设定标签lable为y = x^2\n",
    "y2 = x1*x1\n",
    "plt.plot(x1, y2, label='y = x^2') \n",
    "plt.legend(loc='upper right')  # 改图图例设置在右上角\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(4) 设置图片大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (8, 6)\n",
    "x = [1, 2, 3]\n",
    "y = [2, 4, 6]\n",
    "plt.plot(x, y)\n",
    "plt.show()  # 显示图片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(4) 设置X轴角度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = [1, 2, 3]\n",
    "y = [2, 4, 6]\n",
    "plt.plot(x, y)  # 绘制折线图\n",
    "\n",
    "import pylab as pl\n",
    "pl.xticks(rotation=45)  # 设置角度为45度\n",
    "\n",
    "plt.show()  # 展示图形"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(6) 中文显示问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kJQAROaIvV+1g5OQ0Mrft46e9WjDmvC7UrVEl1mFJCVACEJGI9h7M44F3lvD8Z6tpUb86L1zTh1M7qH5PRaIEICI/8OHSLYx+NYMNuw/wy5Nac/PZx1OzqjYXFY3+j4rIf+3cl8O46YuY+tV62iXVZPJv+tOrler3VFRKACKCu/N2xibueC2DXftzufGM9txwenuqVUmIdWgSICUAkTi3JSub21/LYMbCzXRvXpfnr+5Ll2Z1Yh2WlAIlAJE45e68Mm8d46YvIievgFHnduLaU9pQWcXb4oYSgEgcWrtjP7dOTefjFdvo07oBE4Z3p22SirfFGyUAkTiSX+A89+kqHpyxlEoG44Z24/I+ySreFqeUAETixPLNexg5JY2v1uxi4PFJ3DOsO83rVT/6B6XCCjQBmFlj4B13PzHCsspAZvgFcKO7pwcZj0g8yskr4J+zv+XR91dQs2oCD1/Sg6EnqHibBH8E8Ce+G/i9qBTgZXcfGXAMInErbd0ubpmcxpJNexiS0pSxF3SlYa2qsQ5LyojAEoCZnQHsAzYdpkk/YIiZnQ6kA9e5e15Q8YjEk+zcfB5+dxlPzMmkYa2qTLyiF2d1bRLrsKSMCSQBmFkicDswjNDg75F8CQxy941m9jwwmNAA8oW/ZwQwAiA5OTmIUEUqnLmZ2xk1JY1V2/dzae+W3Dq4M3Wrq3ib/FBQRwCjgMfcfdcRzjOmufvB8PQ8oEPRBu4+EZgIkJqa6kEEKlJR7MnOZcLbS/jX52to2aA6/7q2Lye3bxjrsKQMC+qJj0HADWb2IXCCmT0Zoc0LZtbDzBKAocCCgGIRqfDeX7KZsx7+iJe/WMO1p7Rhxv8O0MZfjiqQIwB3H3BoOpwEHjKz8e4+plCzu4GXAANed/dZQcQiUpHt2JfD3W8sZNo3G+jQqBaPXX8SJybXj3VYUk4E/hyAuw8MT44pMj+D0J1AIlJM7s4baRsZ+/pCsg7k8vufdOC3p7ejamUVb5Po6UEwkXJm0+5sxkzLYNbizaS0qMsDv+5LpyYq3ibFpwQgUk64O//+ci33Tl9MTn4Bowd35lcnt1bxNjlmSgAi5cDq7fsYNSWdzzK307dNA+4fnkLrhjVjHZaUc0oAImVYfoHzzCcr+dPMpVSpVIl7h3Xn0t4tVbxNSoQSgEgZtXTTHm6ZksaCtbv4SadGjB/WjaZ1VbxNSo4SgEgZk5NXwGMfruDvH6ygdrUq/PXSE7igRzMVb5MSF3UCMLOzgFygAHBC9+9XAhLdfUYw4YnEl2/W7mLk5DSWbt7DhSc0444hXThOxdskIMU5AngceBq4CniWUAK4GngKUAIQ+REO5OTz0LtLeerjlTSqXY0nr0xlUJfGsQ5LKrijJgAzGwLkAXsI1ewZHv6vAee7+72BRihSwX367TZGTUlnzY79XNY3mVHndqJONRVvk+BFcwSQAuQANYGuQA2gG6EEkBhcaCIVW1Z2Lve9tZiXv1hLq+Nq8PKv+9G/3XGxDkviyFETwKE9fDO7BthO6GhgG6EEUN3MriQ0sEtukIGKVCSzFm1m9LR0tu45yIgBbblpUEeqJ6qMg5SuqK4BmFlP4F5CG/9HCI3yVRu4E9CxqkiUtu89yNg3FvHGgg10alKbiVek0qNlvViHJXEq2ovANwP/A/QGLgEmAScDXwBVtPcvcmTuzusLNjD29YXsPZjHTYM6cv3AdiRWVhkHiZ0jJgAzqwJ8CNQidC3gNKAZocFb+hEa0D0ReC/QKEXKsQ27DjBmWgbvL9nCCS3r8cDFKXRsXDvWYYkcOQG4e66ZnQ1MBi4DqgGL3P0uMxvo7mNLIUaRcqmgwHnpizVMeHsJeQUFjDmvM786uQ0JKuMgZUQ0F4H3mtnT7j7JzDoBh0aWnhhsaCLl18pt+xg1JY3PV+7gpHbHMeGiFJKPqxHrsES+J9prAMlmVsPdlwBLANz95eDCEimf8vILeOrjlTz07jISK1fi/uHd+VlqS5VxkDIp2gRwEJhtZm8Aj7j7ruBCEimfFm/MYuSUNNLW7ebMLo0ZP7QbjetUi3VYIocV1S0I7v4o0JfQLZ/rzGyZmS03s2VH+pyZNTazr4+w/Ckz+8zMxhyujUhZdzAvn4dmLuX8Rz9m/c4D/O2yE5l4RS9t/KXMi/Y5gLOB/wW2AH3cfVGU3/8nQs8MRPrOi4AEd+9vZk+bWQd3Xx7l94qUCfNX72TUlDSWb9nLsBObc8eQLtSvqQfkpXyI9hTQUOB6d18V7Reb2RnAPmDTYZoMJPQ8AcBM4BTgewnAzEYAIwCSk5Oj/WmRwO3PyePBGUt59tNVNKlTjWd+2ZvTOzWKdVgixRLtUyg3EKoD9ANm1irCvETgdmDUEb6zJrA+PL0D+EHpQ3ef6O6p7p6alJQUZagiwfp4+TbOevgjnvlkFb/o24qZNw3Qxl/KpeKUg74DmB5h/gwz6+bueYXmjQIec/ddR7j7YS/fnR6qRfTJSCQmdh/I5Z7pi5g0bx1tGtbkPyP60betirdJ+RVVAnD3AjOrbGaJ7p5zaL6ZdQaWFtn4AwwCzjCzG4ATzOxJd7+2SJv5hE77zAV6AEuP+a8QCdiMhZu4fVoG2/fl8JvT2vG/gzpQrYqKt0n5VpwjgJbAF2aWCdzn7l8C1wN/LdrQ3QccmjazD4GHzGy8uxe+22caMMfMmgHnEiotIVKmbN1zkLGvL2R6+kY6N63DU1f1pnuLurEOS6RERHsXUC9gibsPCO/1/9nMthIqBPf+kT7r7gPDk2OKzM8ys4HAmcAD7r67mLGLBMbdefXr9dz95iL2H8znj2d15LrT2lElQWcqpeI4WjG4SsC7wAeH5rn7YjO7i9AdPI/+mB939518dyeQSJmwftcBbpuazuxlW+mZHCre1r6RirdJxXO0YnAFZjY8fDF3qJmdA1waXtwTeMzM2rn7t4FHKhKwggLnxc9Xc//bS3Bg7PlduKJ/axVvkwormmJwu8KTzYETgLvdPRPAzO4BRhK+V1+kvPp2615GTUnjy1U7ObVDQ+4d1p2WDVS8TSq2aK8BJADL3H1C4fnunmZmKeFCcfsDiVAkQHn5BUyck8lfZi2nWuVKPHhxChf3aqHibRIXinMX0LOHmX+dNv5SHi3csJuRU9LIWJ/F2V0bM+7CbjRS/R6JI9E+B5BvZj8zs2HhWZWAUe6e4e4LggtPpORl5+bz6PvL+cfsTOrXSOTxy3tybvemsQ5LpNQV5wigFnA5oY3/C8ARK4GKlEXzVu3glilpZG7dx/CeLbh9SGfq1VDxNolPxUkAB9x9DYCZHSj8RLBIWbfvYKh423OfraJZ3eo8d3UfTuuo+lIS346aAMzsaiAHaGJmlwFWZDrR3Z8JNkyRY/fRsq3cOjWdDbsPcGW/Vtx8TidqVS3Ovo9IxRTNv4LmhEYEq0poPGArNJ1A6NSQSJmza38O46cvZvL8dbRNqsmk6/rTu3WDWIclUmZE8xzAOAAzO9PdHwpPn3NoWqQsejt9I7e/tpCd+3P47cB2/O4nKt4mUlRxjoP9MNMiZcaWrGzueG0h7yzcRJemdXj2V73p1lzF20QiKU4CaGFm3xDa+Fc2s1R3nxdMWCLF4+5Mnr+OcW8uIjuvgFvOOZ5fn9pWxdtEjiDqBODuXYIMRORYrd2xn9teTWfO8m30bl2fCcNTaJekS1MiR6NbIaTcKihwnv9sFQ/MWIoBd1/YlV/0bUUlFW8TiUpgCcDMGgC9gK/dfVtQvyPxacWWPYycks781TsZ0DGJe4d1o0V9FW8TKY5AEoCZ1QfeJDSG8ENmdoa7by3SpjKQGX4B3Oju6UHEIxVHbn4BEz/K5K+zllM9MYE//7QHF/VsruJtIscgqCOAFOAP7j43nAx6AjMitHnZ3UcGFINUMBnrd3Pz5DQWb8zivO5NGXtBV5JqV411WCLlViAJwN1nA5jZAKAPcHeEZv2AIWZ2OpBOqKro9waXN7MRhMcaSE5ODiJUKQeyc/P5y6zlPDEnkwY1E/nHL3pxTrcmsQ5LpNwL8hqAAZcAO4HcCE2+BAa5+0Yzex4YDLxeuIG7TwQmAqSmpurZgzj0xcodjJqSRua2ffwstQWjB3ehbo0qsQ5LpEIILAG4uwM3mNk44ALgP0WapLn7wfD0PKBDULFI+bP3YB73v72EF+aupkX96rx4TV9O6dAw1mGJVChBXQQeCWx09+eBesCuCM1eCA8pmQEMBe4NIhYpfz5YuoXRU9PZmJXNr05uzR/POp6aKt4mUuKC+lc1EZhkZtcS2sCvM7Px7j6mUJu7gZcIFZd73d1nBRSLlBM79+Uw7s1FTP16Pe0b1WLyb06iV6v6sQ5LpMIK6iLwTuDMIrPHFGmTQehOIIlz7s709I3c+dpCdh/I5XdntOeGM9pTtbKKt4kEScfVElObs7K5fVoGMxdtpnvzurxwTV+6NKsT67BE4oISgMSEuzNp3lrGT19MTl4Bt57biWtOaUNlFW8TKTVKAFLq1mzfz62vpvHJiu30adOACRd1p62Kt4mUOiUAKTX5Bc6zn67iTzOWklDJGD+0G5f1SVbxNpEYUQKQUrF88x5umZLG12t2MfD4JO4d1p1m9arHOiyRuKYEIIHKySvgH7O/5dH3l1OramX+cskJXHhCMxVvEykDlAAkMAvW7mLklDSWbNrD+T2acef5XWhYS8XbRMoKJQApcQdy8vnLrGU8MSeTpNpVeeLKVM7s0jjWYYlIEUoAUqLmZm5n1JQ0Vm3fz8/7tGTUuZ2pW13F20TKIiUAKRF7snOZ8PYS/vX5GpIb1OCla/tyUnsVbxMpy5QA5Ed7f8lmRr+aweasbK49pQ1/OKsjNRLVtUTKOv0rlWO2fe9B7n5zEa99s4GOjWvx2OUncWKyireJlBdKAFJs7s4baRsZ+/pC9mTn8vufdOCG09uTWFllHETKEyUAKZZNu7MZMy2dWYu30KNFXe6/uC+dmqh4m0h5pAQgUXF3/v3lWu6dvpjcggJGD+7M1ae0IUFlHETKLSUAOapV2/Zx69R0PsvcTr+2DZhwUQqtG9aMdVgi8iMFOSh8A6AX8LW7bwvqdyQ4+QXO0x+v5M/vLqVKpUrcd1F3LkltqeJtIhVEUGMC1wfeBKYDD5nZGe6+NUK7p4AuwHR3Hx9ELHJslm7awy2TF7Bg3W4GdW7E+KHdaVK3WqzDEpESFNQRQArwB3efG04GPYEZhRuY2UVAgrv3N7OnzayDuy8PKB6JUk5eAX//YAWPfbiC2tWq8MjPT+T8lKYq3iZSAQU1JvBsADMbAPQhNAB8UQOBSeHpmcApwPcSgJmNAEYAJCcnBxGqFPLN2l3cMnkByzbv5cITmnHn+V1pUDMx1mGJSECCvAZgwCXATiA3QpOawPrw9A5CRwnf4+4TgYkAqampHkykciAnnz/PXMrTn6ykUe1qPHVVKj/prOJtIhVdYAnA3R24wczGARcA/ynSZC9waESQWoCeIoqBT1dsY9TUdNbs2M9lfZMZdW4n6lRT8TaReBDUReCRwEZ3fx6oB+yK0Gw+odM+c4EewNIgYpHIdh/I5b63FvPvL9fS+rgavPzrfvRvd1yswxKRUhTUEcBEYJKZXQtkAOvMbLy7jynUZhowx8yaAecC/QKKRYp4d9FmxkxLZ+ueg1w3oC3/O6gj1RMTYh2WiJSyoC4C7wTOLDJ7TJE2WWY2MNzuAXffHUQs8p1tew8y9vWFvJm2kU5NavPElamktKgX67BEJEZi+iRwOFFMOmpD+VHcnde+2cBdbyxk78E8/nBmR35zWjsVbxOJcyoFUcFt2HWAMdMyeH/JFk5oWY8HLk6hY+PasQ5LRMoAJYAKqqDAeemLNUx4ewn5Bc7tQ7rwy5Naq3ibiPyXEkAFtHLbPkZOSeOLlTs4uf1x3DcsheTjasQ6LBEpY5QAKpC8/AKe/HglD7+7jMTKlXhgeAo/TW2hMg4iEpESQAWxaEMWI6ekkb5+N2d2acz4od1oXEfF20Tk8JQAyrmDefn87f0VPP7ht9SrUYW/X9aTwd2baK9fRI5KCaAcm796JyOnpLFiy14uOrE5tw/pQn0VbxORKCkBlEP7Dubxp5lLefbTVTStU41nftWb049vFOuwRKScUQIoZ+Ys38qtU9NZt/MAV/ZvxS3ndKJWVf1vFJHi05ajnNi9P5d73lrEpHnraNOwJpOu60+fNg1iHZaIlGNKAOXAOxmbuP21DHbsy+H6ge34/U86UK2KireJyI+jBFCGbd0TKt42PX0jnZvW4emretO9Rd1YhyUiFYQSQBnk7kz9aj13v7mIAzn53Hz28YwY0JYqCSreJiIlRwmgjFm3cz+jX81g9rKt9GpVn/uHp9C+Ua1YhyUiFZASQBlRUOC8+Plq7n97CQ6MPb8LV/ZvTSUVbxORgAQ1JGRd4N9AArAPuMTdc4q0qQxkhl8AN7p7ehDxlHXfbt3LqClpfLlqJ6d2aMi9w7rTsoGKt4lIsII6ArgceMjd3zWzx4FzgNeLtEkBXnb3kQHFUObl5hfwxJxM/jJrOdUqV+LBi1O4uJeKt4lI6QhqSMjHCr1NArZEaNYPGGJmpwPpwHXunhdEPGVRxvrdjJySxsINWZzTtQl3D+1Ko9oq3iYipSfQawBm1h+o7+5zIyz+Ehjk7hvN7HlgMEWOEsxsBDACIDk5OchQS012bj6Pvr+cf8zOpH6NRB6/vCfndm8a67BEJA4FlgDMrAHwKDD8ME3S3P1geHoe0KFoA3efCEwESE1N9SDiLE3zVu3glilpZG7dx8W9WjDmvM7Uq6HibSISG0FdBE4EXgFudffVh2n2gpndA2QAQ4F7g4ilLNh7MI8H31nC83NX06xudZ6/ug8DOibFOiwRiXNBHQFcA/QERpvZaOADoIq7jynU5m7gJcCA1919VkCxxNTsZVu5bWo6G3Yf4Kr+rbn57OOpqeJtIlIGBHUR+HHg8aO0ySB0J1CFtGt/DuPeXMyUr9bRNqkmr1zXn9TWKt4mImWHdkUD8Hb6Rm5/bSE79+dww+ntuPEMFW8TkbJHCaAEbcnK5o7XFvLOwk10bVaH567uTddmKt4mImWTEkAJcHdemb+O8W8uIjuvgJHndOLXp7ahsoq3iUgZpgTwI63dsZ/bXk1nzvJt9G5dnwnDU2iXpOJtIlL2KQEco/wC5/nPVvHgjKUYMO7Crlzet5WKt4lIuaEEcAxWbNnDyCnpzF+9k9M6JnHPsG60qK/ibSJSvigBFENufgH/nP0tj7y3ghpVE3joZz0YdmJzFW8TkXJJCSBK6et2c8uUNBZvzOK8lKaMPb8rSbWrxjosEZFjpgRwFNm5+fxl1nKemJNJg5qJ/POKXpzdtUmswxIR+dGUAI7g88ztjJqazspt+7gktSW3De5M3RpVYh2WiEiJUAKIYE92Lg+8s5QX5q6mRf3qvHhNX07p0DDWYYmIlCglgCI+WLqF0VPT2ZiVzdUnt+GPZ3ekRqJWk4hUPNqyhe3cl8O4Nxcx9ev1dGhUi8m/OYlererHOiwRkcDEfQJwd6anb+TO1xay+0AuvzujPTec0Z6qlVW8TUQqtrhOAJuzshkzLYN3F22me/O6vHhtXzo3rRPrsERESkVcJgB3Z9K8tYyfvpicvAJuPbcT15yi4m0iEl/iLgGs2b6fUVPT+PTb7fRp04D7h6fQpmHNWIclIlLqghoTuC7wbyAB2Adc4u45Edo9BXQBprv7+CBiOSS/wHn201X8acZSEioZ44d247I+ySreJiJxK6hzHpcDD7n7WcAm4JyiDczsIiDB3fsDbc2sQ0CxsGzzHoY//inj3lxE/3bHMfOmAfyinyp3ikh8C2pM4McKvU0CtkRoNhCYFJ6eCZwCLC/pWNbvOsCQRz6mZtUE/nrpCVzQo5mKt4mIEPA1ADPrD9R397kRFtcE1oendwA9I3x+BDACIDk5+ZhiaF6vOndf2JVBXRrTsJaKt4mIHBLYbS9m1gB4FLj6ME32AtXD07UixeLuE9091d1Tk5KSjjmWS/ska+MvIlJEIAnAzBKBV4Bb3X31YZrNJ3TaB6AHsCqIWEREJLKgTgFdQ+iUzmgzGw18AFRx9zGF2kwD5phZM+BcoF9AsYiISARBXQR+HHj8KG2yzGwgcCbwgLvvDiIWERGJLKYPgrn7Tr67E0hEREqRah+IiMQpJQARkTilBCAiEqeUAERE4pS5e6xjiIqZbQUO90xBNBoC20oonJKkuIpHcRWP4iqeihhXK3eP+CRtuUkAP5aZzXP31FjHUZTiKh7FVTyKq3jiLS6dAhIRiVNKACIicSqeEsDEWAdwGIqreBRX8Siu4omruOLmGoCIiHxfPB0BiIhIIXE3KHxpM7PGwGR3P/Uwy38wfjJQAGSGXwA3unt6KYQbc1Gsr+sJrSOAesDnwA3E2fqKZtxt9a3vRLm+YtK3wmOn9AK+dvfSvQXV3SvEC2gMzDlKm6eAz4AxR5pXgjHVB94BvjpCm98CZ4anHwcuIFRK+/5Yri9COwdrgA/Dr+5lYX0Vaf8okBrk+gLqAm8TGrb0VSCxjPStH/SbstC3ollfMepbR11fpd23wr9TH/gUGA2kA0ml2b8qxCkgM6sPPEdomMnDtfnBIPSlMDB9PqE9iqzDNXD3x9z93fDbQ+Mn9wOGmNkXZvaUmZXokVo06wtIAV5294HhV3pZWF+HmFlzoLG7zyPY9XU58JC7nwVsAs6JEEup963D9Jto2gTat4hifRGDvhXN+jqkFPsWhNbFH9z9HmAGkYfGDax/VYgEQHQbjoH8cBD6SPNKjLtneZTjHBQZP/lLYJC79wGqAINLMi6iW1+ROv5Aysj6InRofmjMicDWV5QbjoGUct865CjjbkdqE2jfinJ9lXrfOiSa9UUp9S0Ad5/t7nPNbADQh9AefVEDCah/VYgEEOWGo+gg9I0PM6/URRg/Oc3dN4an5wElvTcUzfqK1PHLyvqqBJxO6PQBBLy+wr95pA1HTPpWFONul3rfKvS7R1pfMelbUa6vWPQtI7RDthPIjdAksP5VIRJAlCINQn/UgemDdpjxk18wsx5mlgAMBRaUdlxE7vgxX19hpwKfe/hEKAGvryg2HKXet6IZdztWfSuK9VXqfSvKccqhlPsWgIfcAKQRuk5TVGD9K54SQKRB6Et1YHoz62Jm44vMLjx+8odmdglwN/AC8A3wmbvPCjKuw4jU8cvC+gI4G/io0PvA1leUG45Y9K2i/ebOstC3olxfsehb0awvKMW+BWBmI83syvDbesCuCM0C618V6kEwM/vQ3QeaWRfgMi80CL2Z1QHmAO/x3SD0XnReMc5Bl3tHWV/dgJcAA15399GR1mFFX1/hWwPv5bs9vw+AKupbkUW5vtS3wsI3ZEwCqgIZwN+Bn5dW/6pQCeBowiv7TOAjd990uHlyeFpfkalv/XhaX4cXVP+KqwQgIiLfiadrACIiUogSgIhInFICEDkG4TtYivuZxCBiETlWSgAiRZjZHWZ27RGW1wfeDT80FO131uK7h4sOzRttZpHu+xYpFUoAIj90EChaKbLyoTow7r6T0O2NPQstTwi/fn3o/nIzm2RmA8P3308BapvZf8zs0NiuOUR+8lOkVOguIBHAzBbz3aP1yYQ2zpuAaoSeunwFuAroGG53INz2eGAdkEfooaF3gLnAFcDf3P2M8Pc/CbxM6GnPc4HbCJVB2AfUBq5393cC/SNFitB4ACIhue4+CMDM/ghscvcXzaw1oQ35U8BTZnYb8I27vxVumwac5O6HEgJmNhGYTujpU8ysM6FEciaQSChZTAAaEnr4JxUdCUgMKAGIhBRE2e4/wB3AW+ENe2bhjX/Ye8BDhI4EAJqEv38Q0IVQcvgd3z8C+ORHRS9yDHQKSAQwswxCp3zgh6eAstx9cKG2zwOPAXcCdxWteGlm/wTqAF+6+0OF5o8FVhAq3pXN948APnb3D4P420QOR0cAIiFXu/sX8INTQNUInfcv7A/AV8DsCBv/ZEJ7+WcBc83scWAYMAJoQeioII1QAvjeR0v47xE5Kh0BiBRhZrcAG939hQjLehAavm8eobuAtgGPufui8PJ/AO+6+xQzG03oFM8j7l5gZncD7xOq3X4XoSSQQ2jglGvd/b3g/zqR7+gIQOSHahG6WPtfZnYC8E9C47aOdvfl4fmDgAlm1gk4291/c+gz4WH+CkskdN4/EZjg7s+Gv2NM0d8TKQ06AhCJQnjUppruvvcwyxPdPSfSsghtqwK4+8ESDFGk2JQARETilJ4EFhGJU0oAIiJxSglARCROKQGIiMSp/wd2aqgaYFDpowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "#plt.rcParams['axes.unicode_minus'] = False  # 解决负号'-'显示为方块的问题\n",
    "\n",
    "x = [1, 2, 3]\n",
    "y = [2, 4, 6]\n",
    "plt.plot(x, y)\n",
    "plt.title('中文标题')  # 添加标题\n",
    "plt.xlabel('中文X轴')  # 添加X轴标签\n",
    "plt.ylabel('中文Y轴')  # 添加Y轴标签\n",
    "plt.show()  # 显示图片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(7) 绘制多图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如下图所示，有时我们需要在一张画布上输出多个图形，在Matplotlib库中有当前的图形（figure）以及当前轴（axes）概念，其对应的就是当前画布以及当前子图，在一张画布（figure）上可以绘制多个子图（axes）。绘制多图通常采用subplot()函数或subplots()函数，"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先来讲解subplot()函数，如下图所示，它通常含有三个参数，子图的行数、列数以及第几个子图，例如subplot(221)表示的就是绘制2行2列的子图（共4个子图），并在第1个子图上进行绘图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3., 0., 0., 0., 0., 1., 0., 0., 0., 1.]),\n",
       " array([2. , 2.2, 2.4, 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 演示代码如下：\n",
    "import matplotlib.pyplot as plt\n",
    "# 绘制第一个子图：折线图\n",
    "ax1 = plt.subplot(221)  \n",
    "plt.plot([1, 2, 3], [2, 4, 6])  # 这里plt其实也可以换成ax1\n",
    "\n",
    "# 绘制第二个子图：柱状图\n",
    "ax2 = plt.subplot(222)  \n",
    "plt.bar([1, 2, 3], [2, 4, 6])\n",
    "\n",
    "# 绘制第三个子图：散点图\n",
    "ax3 = plt.subplot(223)  \n",
    "plt.scatter([1, 3, 5], [2, 4, 6])\n",
    "\n",
    "# 绘制第四个子图：直方图\n",
    "ax4 = plt.subplot(224)  \n",
    "plt.hist([2, 2, 2, 3, 4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了加强大家对画布（figure）和子图（axes）的理解，我们通过下面的代码来做一个简单演示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cb635bbf60>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (8, 4) # 设置画布大小\n",
    "\n",
    "plt.figure(1)  # 第一张画布\n",
    "ax1 = plt.subplot(121)  # 第一张画布的第一个子图\n",
    "plt.plot([1, 2, 3], [2, 4, 6])  # 这里的plt可以换成ax1\n",
    "\n",
    "ax2 = plt.subplot(122)  # 第一张画布的第二个子图\n",
    "plt.plot([2, 4, 6], [4, 8, 10])\n",
    "\n",
    "plt.figure(2)  # 第二张画布\n",
    "plt.plot([1, 2, 3], [4, 5, 6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在使用subplot()函数的时候，每次在新的子图上画图时，都得调用subplot()函数，例如第四个子图就得写成ax4 = plt.subplot(224)，那有没有什么办法，一次性就生成多个子图呢？这时候就可以用到subplots()函数，代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3., 0., 0., 0., 0., 1., 0., 0., 0., 1.]),\n",
       " array([2. , 2.2, 2.4, 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(10, 8)) \n",
    "ax1, ax2, ax3, ax4 = axes.flatten()\n",
    "ax1.plot([1, 2, 3], [2, 4, 6])  # 绘制第一个子图\n",
    "ax2.bar([1, 2, 3], [2, 4, 6])  # 绘制第二个子图\n",
    "ax3.scatter([1, 3, 5], [2, 4, 6])  # 绘制第三个子图\n",
    "ax4.hist([2, 2, 2, 3, 4])  # 绘制第四个子图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此外，如果要在subplot()函数或者subplots()函数生成的子图中设置子图标题、X轴标签或Y轴标签，得通过set_title()函数、set_xlabel()函数、set_ylabel()函数进行设置，演示代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3., 0., 0., 0., 0., 1., 0., 0., 0., 1.]),\n",
       " array([2. , 2.2, 2.4, 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "fig, axes = plt.subplots(2, 2, figsize=(10, 8)) \n",
    "ax1, ax2, ax3, ax4 = axes.flatten()\n",
    "ax1.plot([1, 2, 3], [2, 4, 6])  # 绘制第一个子图\n",
    "ax1.set_title('子图1')\n",
    "ax1.set_xlabel('日期')\n",
    "ax1.set_ylabel('分数')\n",
    "ax2.bar([1, 2, 3], [2, 4, 6])  # 绘制第二个子图\n",
    "ax3.scatter([1, 3, 5], [2, 4, 6])  # 绘制第三个子图\n",
    "ax4.hist([2, 2, 2, 3, 4])  # 绘制第四个子图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
